scholarly journals Tuning Genetic Algorithm Parameters to Improve Convergence Time

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Maria Angelova ◽  
Tania Pencheva
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Method ◽  
Convergence Time ◽  
Generation Gap ◽  
Genetic Operators ◽  
Time Saving ◽  
Fermentation Processes ◽  
Highly Nonlinear ◽  
Crossover And Mutation

Fermentation processes by nature are complex, time-varying, and highly nonlinear. As dynamic systems their modeling and further high-quality control are a serious challenge. The conventional optimization methods cannot overcome the fermentation processes peculiarities and do not lead to a satisfying solution. As an alternative, genetic algorithms as a stochastic global optimization method can be applied. For the purpose of parameter identification of a fed-batch cultivation ofS. cerevisiaealtogether four kinds of simple and four kinds of multipopulation genetic algorithms have been considered. Each of them is characterized with a different sequence of implementation of main genetic operators, namely, selection, crossover, and mutation. The influence of the most important genetic algorithm parameters—generation gap, crossover, and mutation rates has—been investigated too. Among the considered genetic algorithm parameters, generation gap influences most significantly the algorithm convergence time, saving up to 40% of time without affecting the model accuracy.

Optimization of Genetic Operators for Scheduling Problems

2007 ◽  
Vol 11 (9) ◽  
pp. 1092-1098 ◽  
Author(s):  
António Ferrolho ◽  
◽  
Manuel Crisóstomo ◽  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Job Scheduling ◽  
Software Tool ◽  
Scheduling Problems ◽  
Genetic Operators ◽  
Mutation Operators ◽  
Crossover And Mutation

Genetic algorithms (GA) can provide good solutions for scheduling problems. But, when a GA is applied to scheduling problems various crossovers and mutations operators can be applicable. This paper presents and examines a new concept of genetic operators for scheduling problems. A software tool called hybrid and flexible genetic algorithm (HybFlexGA) was developed to examine the performance of various crossover and mutation operators by computing simulations of job scheduling problems.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

scholarly journals SCHEDULLING MODEL OF REPLENISHMENT AT SEA FOR STRICKING FORCE UNIT IN SEA OPERATION USING GENETIC ALGORITHM

JOURNAL ASRO ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Aris Tri Ika R ◽  
Benny Sukandari ◽  
Okol Sri Suharyo ◽  
Ayip Rivai Prabowo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Time Windows ◽  
Lubricating Oil ◽  
Optimal Time ◽  
Routing Problem ◽  
Scheduling Model ◽  
Force Unit ◽  
Replenishment At Sea ◽  
Crossover And Mutation

Navy as a marine core in the defense force is responsible for providing security for realizing stability and security of the country.  At any time there was an invasion of other countries past through sea,  TNI AL must be able to break the enemy resistance line through a sea operation to obtain the sea superiority. But this time the endurance of Striking force Unit at only 7-10 days and required replenishment at sea to maximize the presence in the theater of operations to meet a demand of the logistics: HSD, Freshwater, Lubricating Oil, foodstuffs and amonisi. For the optimal replenishment at sea required scheduling model supporting unit to get the minimum time striking force unit was on node rendezvous. Replenishment at sea scheduling model for striking force unit refers to the problems Vehicle routing problem with time windows using Genetic Algorithms. These wheelbase used is roulette for reproduction, crossover, and mutation of genes. Genetic algorithms have obtained optimum results in the shortest route provisioning scenario uses one supporting unit with a total time of 6.89 days. In scenario two supporting unit with minimal time is 4.97 days. In the scenario, the changing of the node replenishment Genetic Algorithm also get optimal time is 4.97 days with two supporting units. Research continued by changing the parameters of the population, the probability of crossover and mutation that can affect the performance of the genetic algorithm to obtain the solution. Keywords: Genetic Algorithm, Model Scheduling, Striking Force unit

Optimization of Radial Cams with Translating Roller Follower through Genetic Algorithms

2015 ◽  
Vol 783 ◽  
pp. 83-94
Author(s):  
Alberto Borboni
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Contact Pressure ◽  
Optimization Problem ◽  
Optimization Method ◽  
Optimization Process ◽  
Radius Of Curvature ◽  
Design Problems ◽  
Computational Optimization ◽  
Different Types

In this work, the optimization problem is studied for a planar cam which rotates around its axis and moves a centered translating roller follower. The proposed optimization method is a genetic algorithm. The paper deals with different design problems: the minimization of the pressure angle, the maximization of the radius of curvature and the minimization of the contact pressure. Different types of motion laws are tested to found the most suitable for the computational optimization process.

scholarly journals Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real-Coded Genetic Algorithm (RCGA)

2018 ◽  
Vol 7 (4.30) ◽  
pp. 443 ◽  
Author(s):  
Ainul, H.M.. Y ◽  
Salleh, S. M ◽  
Halib, N ◽  
Taib, H. ◽  
Fathi, M. S
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Technique ◽  
Convergence Time ◽  
Generation Gap ◽  
Model Order ◽  
Real Coded Genetic Algorithm ◽  
The Mean ◽  
Conventional Genetic Algorithm

System identification is a method to build a model for a dynamic system from the experimental data. In this paper, optimization technique was applied to optimize the objective function that lead to satisfying solution which obtain the dynamic model of the system. Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. Hence, the model of the plant was represented by the transfer function from the identified parameters obtained from the optimization process. For performance analysis of toothbrush rig parameter estimation, there were six different model orders have been considered where each of model order has been analyzed for 10 times. The influence of conventional genetic algorithm parameter - generation gap has been investigated too. The statistical analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). The validation test-through correlation analysis was used to validate the model. The model of model order 2 is chosen as the best model as it has fulfilled the criteria involved in selecting the accurate model. Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.

scholarly journals A Two-Dimensional Genetic Algorithm and Its Application to Aircraft Scheduling Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Ming-Wen Tsai ◽  
Tzung-Pei Hong ◽  
Woo-Tsong Lin
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Scheduling Problem ◽  
Two Dimensional ◽  
Dimensional Representation ◽  
One Dimensional ◽  
Time Table ◽  
Feasible Solutions ◽  
Crossover And Mutation ◽  
Airline Company

Genetic algorithms have become increasingly important for researchers in resolving difficult problems because they can provide feasible solutions in limited time. Using genetic algorithms to solve a problem involves first defining a representation that describes the problem states. Most previous studies have adopted one-dimensional representation. Some real problems are, however, naturally suitable to two-dimensional representation. Therefore, a two-dimensional encoding representation is designed and the traditional genetic algorithm is modified to fit the representation. Particularly, appropriate two-dimensional crossover and mutation operations are proposed to generate candidate chromosomes in the next generations. A two-dimensional repairing mechanism is also developed to adjust infeasible chromosomes to feasible ones. Finally, the proposed approach is used to solve the scheduling problem of assigning aircrafts to a time table in an airline company for demonstrating the effectiveness of the proposed genetic algorithm.

scholarly journals Implementation of Genetic Algorithms and Momentum Backpropagation in Classification of Subtype Cells Acute Myeloid Leukimia

2020 ◽  
Vol 14 (2) ◽  
pp. 189
Author(s):  
Dian Mustikaningrum ◽  
Retantyo Wardoyo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
White Blood Cells ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Learning Rate ◽  
Algorithm Optimization ◽  
Selection Parameter ◽  
Acute Myeloid ◽  
Random Trial

 Acute Myeloid Leukimia (AML) is a type of cancer which attacks white blood cells from myeloid. AML subtypes M1, M2, and M3 are affected by the same type of cells called myeloblasts, so it needs more detailed analysis to classify.Momentum Backpropagation  is used to classified. In its application, optimal selection of architecture, learning rate, and momentum is still done by random trial. This is one of the disadvantage of Momentum Backpropagation. This study uses a genetic algorithm (GA) as an optimization method to get the best architecture, learning rate, and momentum of artificial neural network. Genetic algorithms are one of the optimization techniques that emulate the process of biological evolution.The dataset used in this study is numerical feature data resulting from the segmentation of white blood cell images taken from previous studies which has been done by Nurcahya Pradana Taufik Prakisya. Based on these data, an evaluation of the Momentum Backpropagation process was conducted the selection parameter in a random trial with the genetic algorithm. Furthermore, the comparison of accuracy values was carried out as an alternative to the ANN learning method that was able to provide more accurate values with the data used in this study.The results showed that training and testing with genetic algorithm optimization of ANN parameters resulted in an average memorization accuracy of 83.38% and validation accuracy of 94.3%. Whereas in other ways, training and testing with momentum backpropagation random trial resulted in an average memorization accuracy of 76.09% and validation accuracy of 88.22%.

scholarly journals Scheduling Courses using Genetic Algorithms

2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Schedule ◽  
Heuristic Method ◽  
Biological Evolution ◽  
Time Data ◽  
Fitness Evaluation ◽  
Class Schedule ◽  
Student College ◽  
Crossover And Mutation

Preparation of courses at every university is done by hand. This method has limitations that often cause collisions schedule. In lectures and lab scheduling frequent collision against the faculty member teaching schedule, collisions on the class schedule and student, college collision course with lab time, the allocation of the use of the rooms were not optimal. Heuristic method of genetic algorithm based on the mechanism of natural selection; it is a process of biological evolution. Genetic algorithms are used to obtain optimal schedule that consists of the initialization process of the population, fitness evaluation, selection, crossover, and mutation. Data used include the teaching of data, the data subjects, the room data and time data retrieved from the database of the Faculty of Computer Science, Universitas Pembangunan Panca Budi. The data in advance through the stages of the process of genetic algorithms to get optimal results The results of this study in the form of a schedule of courses has been optimized so that no error occurred and gaps.

scholarly journals Computing strong metric dimension of some special classes of graphs by genetic algorithms

2008 ◽  
Vol 18 (2) ◽  
pp. 143-151 ◽  
Author(s):  
Jozef Kratica ◽  
Vera Kovacevic-Vujcic ◽  
Mirjana Cangalovic
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Graph Coloring ◽  
Crew Scheduling ◽  
Metric Dimension ◽  
Hard Problem ◽  
Genetic Operators ◽  
Strong Metric Dimension ◽  
Np Hard Problem ◽  
Special Classes

In this paper we consider the NP-hard problem of determining the strong metric dimension of graphs. The problem is solved by a genetic algorithm that uses binary encoding and standard genetic operators adapted to the problem. This represents the first attempt to solve this problem heuristically. We report experimental results for the two special classes of ORLIB test instances: crew scheduling and graph coloring.

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